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arXiv Machine Learning By Amirmohammad Farzaneh, Osvaldo Simeone

Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

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The paper investigates risk‑averse decision making where an agent chooses actions under uncertainty about the system state, using optimized certainty equivalent (OCE) metrics that encompass mean‑variance risk and CVaR. For known distributions, the optimal policy simplifies to a prediction‑set‑based solution for CVaR, linking it to conformal prediction sets. When distributions are unknown, the authors propose a data‑driven calibration method that employs a synthetic likelihood model and held‑out data to achieve high‑probability OCE risk control, and they demonstrate the method on two wireless beamforming scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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